code: def evaluate(
      model,
      eval_dataset,
      threshold=0.1,
      nms_kernel=7,
      top_k=200,
      num_workers=0,
      print_detail=True,
  ):
      """
      Launch evaluation.
  
      Args:
          model（nn.Layer): A sementic segmentation model.
          eval_dataset (paddle.io.Dataset): Used to read and process validation datasets.
          threshold (float, optional): Threshold applied to center heatmap score. Defalut: 0.1.
          nms_kernel (int, optional): NMS max pooling kernel size. Default: 7.
          top_k (int, optional): Top k centers to keep. Default: 200.
          num_workers (int, optional): Num workers for data loader. Default: 0.
          print_detail (bool, optional): Whether to print detailed information about the evaluation process. Default: True.
  
      Returns:
          dict: Panoptic evaluation results which includes PQ, RQ, SQ for all, each class, Things and stuff.
          dict: Semantic evaluation results which includes mIoU, fwIoU, mACC and pACC.
          dict: Instance evaluation results which includes mAP and mAP50, and also AP and AP50 for each class.
  
      """
      model.eval()
      nranks = paddle.distributed.ParallelEnv().nranks
      local_rank = paddle.distributed.ParallelEnv().local_rank
      batch_sampler = paddle.io.BatchSampler(
          eval_dataset, batch_size=1, shuffle=False, drop_last=False
      )
      loader = paddle.io.DataLoader(
          eval_dataset,
          batch_sampler=batch_sampler,
          num_workers=num_workers,
          return_list=True,
      )
  
      total_iters = len(loader)
      semantic_metric = SemanticEvaluator(
          eval_dataset.num_classes,
          ignore_index=eval_dataset.ignore_index,
      )
      instance_metric_AP50 = InstanceEvaluator(
          eval_dataset.num_classes,
          overlaps=0.5,
          thing_list=eval_dataset.thing_list,
      )
      instance_metric_AP = InstanceEvaluator(
          eval_dataset.num_classes,
          overlaps=list(np.arange(0.5, 1.0, 0.05)),
          thing_list=eval_dataset.thing_list,
      )
      panoptic_metric = PanopticEvaluator(
          num_classes=eval_dataset.num_classes,
          thing_list=eval_dataset.thing_list,
          ignore_index=eval_dataset.ignore_index,
          label_divisor=eval_dataset.label_divisor,
      )
  
      if print_detail:
          logger.info(
              "Start evaluating (total_samples={}, total_iters={})...".format(
                  len(eval_dataset), total_iters
              )
          )
      progbar_val = progbar.Progbar(target=total_iters, verbose=1)
      reader_cost_averager = TimeAverager()
      batch_cost_averager = TimeAverager()
      batch_start = time.time()
      with paddle.no_grad():
          for iter, data in enumerate(loader):
              reader_cost_averager.record(time.time() - batch_start)
              im = data[0]
              raw_semantic_label = data[1]  # raw semantic label.
              raw_instance_label = data[2]
              raw_panoptic_label = data[3]
              ori_shape = raw_semantic_label.shape[-2:]
  
              (
                  semantic,
                  semantic_softmax,
                  instance,
                  panoptic,
                  ctr_hmp,
              ) = infer.inference(
                  model=model,
                  im=im,
                  transforms=eval_dataset.transforms.transforms,
                  thing_list=eval_dataset.thing_list,
                  label_divisor=eval_dataset.label_divisor,
                  stuff_area=eval_dataset.stuff_area,
                  ignore_index=eval_dataset.ignore_index,
                  threshold=threshold,
                  nms_kernel=nms_kernel,
                  top_k=top_k,
                  ori_shape=ori_shape,
              )
              semantic = semantic.squeeze().numpy()
              semantic_softmax = semantic_softmax.squeeze().numpy()
              instance = instance.squeeze().numpy()
              panoptic = panoptic.squeeze().numpy()
              ctr_hmp = ctr_hmp.squeeze().numpy()
              raw_semantic_label = raw_semantic_label.squeeze().numpy()
              raw_instance_label = raw_instance_label.squeeze().numpy()
              raw_panoptic_label = raw_panoptic_label.squeeze().numpy()
  
              # update metric for semantic, instance, panoptic
              semantic_metric.update(semantic, raw_semantic_label)
  
              gts = instance_metric_AP.convert_gt_map(
                  raw_semantic_label, raw_instance_label
              )
              # print([i[0] for i in gts])
              preds = instance_metric_AP.convert_pred_map(
                  semantic_softmax, panoptic
              )
              # print([(i[0], i[1]) for i in preds ])
              ignore_mask = (
                  raw_semantic_label == eval_dataset.ignore_index
              )
              instance_metric_AP.update(
                  preds, gts, ignore_mask=ignore_mask
              )
              instance_metric_AP50.update(
                  preds, gts, ignore_mask=ignore_mask
              )
  
              panoptic_metric.update(panoptic, raw_panoptic_label)
  
              batch_cost_averager.record(
                  time.time() - batch_start, num_samples=len(im)
              )
              batch_cost = batch_cost_averager.get_average()
              reader_cost = reader_cost_averager.get_average()
  
              if local_rank == 0:
                  progbar_val.update(
                      iter + 1,
                      [
                          ("batch_cost", batch_cost),
                          ("reader cost", reader_cost),
                      ],
                  )
              reader_cost_averager.reset()
              batch_cost_averager.reset()
              batch_start = time.time()
  
      semantic_results = semantic_metric.evaluate()
      panoptic_results = panoptic_metric.evaluate()
      instance_results = OrderedDict()
      ins_ap = instance_metric_AP.evaluate()
      ins_ap50 = instance_metric_AP50.evaluate()
      instance_results["ins_seg"] = OrderedDict()
      instance_results["ins_seg"]["mAP"] = ins_ap["ins_seg"]["mAP"]
      instance_results["ins_seg"]["AP"] = ins_ap["ins_seg"]["AP"]
      instance_results["ins_seg"]["mAP50"] = ins_ap50["ins_seg"]["mAP"]
      instance_results["ins_seg"]["AP50"] = ins_ap50["ins_seg"]["AP"]
  
      if print_detail:
          logger.info(panoptic_results)
          print()
          logger.info(semantic_results)
          print()
          logger.info(instance_results)
          print()
  
          pq = panoptic_results["pan_seg"]["All"]["pq"]
          miou = semantic_results["sem_seg"]["mIoU"]
          map = instance_results["ins_seg"]["mAP"]
          map50 = instance_results["ins_seg"]["mAP50"]
          logger.info(
              "PQ: {:.4f}, mIoU: {:.4f}, mAP: {:.4f}, mAP50: {:.4f}".format(
                  pq, miou, map, map50
              )
          )
  
      return panoptic_results, semantic_results, instance_results
  
file_path: PaddleSeg/contrib/PanopticDeepLab/core/val.py
imports: import os
  from collections import OrderedDict
  import numpy
  import time
  import paddle
  import paddle.nn.functional
  from paddleseg.utils import TimeAverager, calculate_eta, logger, progbar
  from utils.evaluation import SemanticEvaluator, InstanceEvaluator, PanopticEvaluator
  from core import infer
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, eval_dataset, threshold, nms_kernel, top_k, num_workers, print_detail) and returns: panoptic_results,  semantic_results,  instance_results